Estimation of Lidar backscatter gap, and aerosol size distribution using Artificial Neural Network Algorithms

dc.contributor.advisor Parsiani, Hamed González-Chévere, David College of Engineering en_US
dc.contributor.committee Rodríguez Solís, Rafael A.
dc.contributor.committee Ramírez, Nazario D.
dc.contributor.department Department of Electrical and Computer Engineering en_US
dc.contributor.representative De La Rosa Ricciardi, Evi 2018-09-20T17:21:39Z 2018-09-20T17:21:39Z 2017
dc.description.abstract This work presents the design and implementation of two Artificial Neural Network (ANN) algorithms to (1) estimate data-gap in Lidar 1064 nm, and (2) determine and plot Aerosol Size Distribution (ASD) based on four available wavelengths at the University of Puerto Rico, Mayaguez (UPRM). In (1), the network was trained using Ceilometer data at 910nm as input and available Lidar data at 1064 nm from the same time and range as target. Results of the error analysis show good matches with better than 0.82 of correlation and 0.844 of Root mean Square Error (RMSE) values. In (2), the backscatter column profile data at the three Lidar wavelengths (355, 532, 1064 nms) and single wavelength Ceilometer (910 nm) were used to derive their respective Aerosol Optical Depth (AOD) values. To determine ASD using Lidars, an ANN was trained using the Aerosol Robotic Network (AERONET) data. Subsequently, the AODs from Lidars (obtained at 355, 532, 910, and 1064nms) were used as inputs to the trained ANN to generate ASD at the output. Comparison between the estimated ASD based on Lidar’s four wavelengths and AERONET ASD of eight wavelengths showed good results with RMSE better than 0.016. en_US
dc.description.graduationYear 2017 en_US
dc.description.sponsorship NOAA-CREST under the research grant #NA06OARR4810162 and the UPRM ECE (Electrical and Computer Engineering) department en_US
dc.language.iso en en_US
dc.rights.holder (c) 2017 David González Chévere en_US
dc.rights.license All rights reserved en_US
dc.subject Lidar backscatter gap en_US
dc.subject Artificial Neural Network Algorithms en_US
dc.subject.lcsh Backscattering en_US
dc.subject.lcsh Neural networks (Computer science) en_US
dc.subject.lcsh Wavelengths en_US
dc.title Estimation of Lidar backscatter gap, and aerosol size distribution using Artificial Neural Network Algorithms en_US
dc.type Project Report en_US
dspace.entity.type Publication Electrical Engineering en_US M.E. en_US
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